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URN: urn:nbn:de:0030-drops-80529
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The EfProb Library for Probabilistic Calculations

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Abstract

EfProb is an abbreviation of Effectus Probability. It is the name of a library for probability calculations in Python. EfProb offers a uniform language for discrete, continuous and quantum probability. For each of these three cases, the basic ingredients of the language are states, predicates, and channels. Probabilities are typically calculated as validities of predicates in states. States can be updated (conditioned) with predicates. Channels can be used for state transformation and for predicate transformation. This short paper gives an overview of the use of EfProb.

BibTeX - Entry

@InProceedings{cho_et_al:LIPIcs:2017:8052,
  author =	{Kenta Cho and Bart Jacobs},
  title =	{{The EfProb Library for Probabilistic Calculations}},
  booktitle =	{7th Conference on Algebra and Coalgebra in Computer Science (CALCO 2017)},
  pages =	{25:1--25:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-033-0},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{72},
  editor =	{Filippo Bonchi and Barbara K{\"o}nig},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/8052},
  URN =		{urn:nbn:de:0030-drops-80529},
  doi =		{10.4230/LIPIcs.CALCO.2017.25},
  annote =	{Keywords:  probability, embedded language, effectus theory}
}

Keywords: probability, embedded language, effectus theory
Seminar: 7th Conference on Algebra and Coalgebra in Computer Science (CALCO 2017)
Issue date: 2017
Date of publication: 2017


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